Neural networks are useful tools for machine learning. Inspired by studies of nerve and brain tissue, designers have created a variety of neural network architectures. In many commonly-used architectures, the neural networks are trained with a set of input signals and corresponding set of desired output signals. The neural networks “learn” the relationships between input and output signals, and thereafter these networks can be applied to a new input signal set to predict corresponding output signals. In this capacity, neural networks have found many applications including identifying credit risks, appraising real estate, predicting solar flares, regulating industrial processes, and many more.
In many applications, there are a large number of possible input parameters that can be selected in order to predict desired output parameters. Optimizing the choice of input parameters can assist in producing stable and accurate predictions. Unfortunately, the input optimization process can be difficult.
While the invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the figures and will herein be described in detail. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims.